Imagine easily enriching data streams and building stream processing applications in the cloud, without worrying about capacity planning, infrastructure and runtime upgrades, or performance monitoring. That's where our serverless Apache Flink® service comes in, as announced at this year’s Current | The Next Generation of Kafka Summit.

In 2023, generative AI took the spotlight, emerging as the most talked-about technology of the year. This content creating powerhouse can do everything from text, image, and video generation to answering questions through natural language queries. And its potential uses are vast. While many industries are still in the experimental phase, the insurance sector is poised to benefit significantly from the integration of artificial intelligence into its ecosystem.

Platform Engineering vs. DevOps: While they both share similar goals, here are 3 key differentiating principles. See how they apply to your tech stack.

At Integrate.io, we work with companies that build data pipelines. Some start cloud-native on platforms like Amazon Redshift, while others migrate from on-premise or hybrid solutions. What they all have in common is the one question they ask us at the very beginning: And so that’s why we decided to compile and publish a list of publicly available blog posts about how companies build their data pipelines.

Customer services now constitute one of the key distinguishing factors of banks in an ever-changing environment. With increasing customer requirements, banks must adopt and incorporate novel technological methods to provide customized services. A very promising technology in this regard is generative AI. Generative AI in core banking systems will enable banks to transform their customer services by increasing efficiencies and building meaningful customer relationships.

You need metrics to do your job well as a marketer but getting clear, meaningful metrics is a huge challenge. While digital advertisers and paid media professionals are on the hook to build ample sales pipeline and maximize return on ad spend (ROAS), they’re also expected to deliver personalized advertising content while navigating evolving privacy requirements and adhering to consumer expectations—all while extracting insights from siloed ad platforms.

The data mesh approach has gained popularity over the last couple of years as organizations look for reliable ways to break down data silos. At first, data lakes looked like a good way to improve data management and make information more discoverable. Unfortunately, data lakes — and data warehouses — don’t always conform to business needs. They’re often slow and even unresponsive to queries. Potentially even worse, they can still lead to data silos.

If your business deals in large volumes of data, chances are that you are at least somewhat familiar with PostgreSQL, or you may use it actively in your data management strategy. PostgreSQL is a powerful relational database system, and it is completely open source. As of 2019, PostgreSQL was the second-most-common database solution for professionals. Thirty-four percent of more than 75,000 Respondents reported that it was their first choice.

Kipi.bi and Hevo Data have been bringing increased efficiency and maturity into enterprise organizations’ data stacks for years, and both teams are thrilled to formalize their partnership! Both organizations share a keen dedication to enabling data-driven decision-making by allowing businesses to leverage the power of modern data solutions.

Microservices architecture has emerged as a popular approach for developing software applications in recent years. Instead of building monolithic applications, microservices architecture involves breaking down an application into smaller, independent services that can be developed, deployed, and scaled independently. This approach offers a number of benefits, including increased flexibility, better scalability, and improved fault tolerance.